Method and system of ranking search results, and method and system of optimizing search result ranking

ABSTRACT

The present disclosure provides techniques to rank search results. The techniques may include acquiring a set of primitive characteristics and extracting effective characteristics from the set of primitive characteristics based on historical transaction data. The effective characteristics include characteristics that can have an influence on ranking of search results. The techniques may also include determining an initial weight of each of the effective characteristics based on the historical transaction data, and training the initial weight using the historical transaction data and a predetermined training model to obtain a final weight. Based on the final weight, the search results may be ranked. In some aspects, the techniques may also optimize the ranking to ensure the objectivity and accuracy of ranking results.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to Chinese Patent Application No.201210254425.6, filed on Jul. 20, 2012, entitled “Method and System ofRanking Search Results, and Method and System of Optimizing SearchResult Ranking,” which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates to computer data processing, andparticularly relates to ranking and optimizing search results.

BACKGROUND

With the development of Electronic Commerce (e-commerce), more and moreusers purchase items using e-commerce services. In general, ane-commerce website owns tens of millions or even hundreds of millions ofitems, and thus the users have to search the website to find desireditems. A server associated with the website may perform searches basedon keywords provided by the users, and return results corresponding tothe keywords.

In response to a keyword, the server may produce a larger number ofsearch results. Accordingly, the search results need to be sorted and/orranked according to a certain order during presentation. The server maytake comprehensive consideration into how to rank these search results.For example, search results may be ranked according to a correlationbetween the search results and a keyword, previous click-through rates,previous deals associated with the search results, and etc. For ane-commerce website, to improve the sales volume of a commodity, theserver may also consider deal feasibilities (e.g., deal conversion ratesand positive feedback rates of search results).

Currently, a server of an e-commerce website ranks search results basedon the correlation and deal feasibility predictions that are generallyobtained based on manual analysis on historical data, empiricaldetermination of commodity characteristics and weights of the searchresults (i.e. a specific commodity), and/or calculations according to acertain formula. Commodity characteristics refer to factors that arecapable of affecting the deal feasibility of the commodity (e.g., salevolumes, positive feedback rates, and deal conversion rates). Sincedetermination of characteristics and weights by empirical setting isrelatively random and subjective, errors often occur. The returnedsearch results may differ significantly from what the users desired, orranking of the search results may not satisfy the users. Because theserver may only return a certain number of search results, the users maynot receive their desired results. To obtain their desired results, theusers may modify keywords and re-submit queries. This causes the serverto have increased data transmission, which undoubtedly increases theburden on the server and occupies a lot of network resources or evenleads to network congestion. Meanwhile, this also indicates that thesearch results returned by the server have a large amount of irrelevantdata, and server resources and network resources are therefore wasted.

SUMMARY

The present disclosure provides a method and system of ranking searchresults and an optimization method and system of ranking search results.Embodiments of the present disclosure solve the problem of increasedburden on a server and of network congestion as described above.

Embodiments of the present disclosure relate to a method of rankingsearch results. The embodiments include acquiring a set of primitivecharacteristics. In some embodiments, the primitive characteristicsinclude preset characteristics that may have an influence on ranking ofsearch results. The embodiments may also include extracting effectivecharacteristics from the set of primitive characteristics based onhistorical transaction data, wherein the effective characteristics referto characteristics determined based on the historical transaction datathat may have an influence on ranking of search results, determining aninitial weight of each of the effective characteristics based on thehistorical transaction data, training the initial weight using thehistorical transaction data and a predetermined training model to obtaina final weight, and ranking the search results based on the final weightof the effective characteristic.

Further, the extracting effective characteristics from the set ofprimitive characteristics based on historical transaction data comprisesselecting two groups of test products based on historical transactiondata, one of which being products with deal records and the other ofwhich being products without deal records, respectively extractingassociated data of the two groups of test products within a certain timeperiod from the historical transaction data and calculating acharacteristic value of each primitive characteristic of the two groupsof test products using the associated data, and comparing characteristicvalues of the same primitive characteristic of the two groups of testproducts, and if a difference value thereof exceeds a threshold value,selecting the primitive characteristic as an effective characteristic.

Further, the extracting effective characteristics from the set ofprimitive characteristics based on historical transaction data comprisesextracting transaction data within a predetermined time period fromhistorical transaction data and calculating a deal conversion rate ofeach product within the predetermined time period, selecting two groupsof products with a difference value of deal conversion rates greaterthan a threshold value as test products, extracting transaction data ofthe two groups of test products within a certain time period followingthe predetermined time period from the historical transaction data andcalculating a characteristic value of each primitive characteristic in aset of primitive characteristics of the two groups of test products, andcomparing characteristic values of the same primitive characteristic ofthe two groups of test products, and if a difference value thereofexceeds a threshold value, selecting the primitive characteristic as aneffective characteristic.

Further, the determining an initial weight of each of the effectivecharacteristics based on the historical transaction data, and trainingthe initial weight using the historical transaction data and a trainingmodel to obtain a final weight comprises determining an initial weightof an effective characteristic, substituting the historical transactiondata and the initial weight into a predetermined training model tocalculate theoretical data, and comparing the theoretical data withactual data, and if a difference there between is within a predeterminedrange, determining that the initial weight is a final weight of theeffective characteristic, if not, returning to the step of determiningan initial weight of an effective characteristic.

Further, the ranking the search results based on the final weight of theeffective characteristic comprises determining actual effectivecharacteristic values of the search results, calculating predicted dealconversion rates of the search results based on the final weight of theeffective characteristic and the actual effective characteristic values,and ranking the search results based on the predicted deal conversionrates.

Embodiments of the present disclosure relate to an optimization methodof ranking search results. The embodiments may include respectivelyacquiring each group of candidate weight values of effectivecharacteristics of search results, calculating theoretical rankingscores of search results at a certain predetermined time pointrespectively using each of the candidate weight values, and ranking thesearch results based on the theoretical ranking scores to obtain eachgroup of ranking results, respectively acquiring a predetermined numberof search results ranked higher in each group of ranking results, andacquiring transaction data of the search results after the predeterminedtime point, calculating actual ranking scores of a predetermined numberof search results ranked higher in each group of ranking results basedon the transaction data, and selecting candidate weight valuescorresponding to a group of ranking results with the highest actualranking score as final weight values of the effective characteristics.

Further, the theoretical ranking score is a predicted value of a singlecharacteristic or a predicted value of a combination of characteristics,and the actual ranking score is an actual value of a singlecharacteristic or an actual value of a combination of characteristicsthat is corresponding to the theoretical ranking score.

Further, the theoretical ranking score is a predicted deal conversionrate and the actual ranking score is an actual deal conversion rate orthe theoretical ranking score is a predicted positive feedback rate andthe actual ranking score is an actual positive feedback rate.

Further, the selecting candidate weight values corresponding to a groupof ranking results with the highest actual ranking score as final weightvalues of the effective characteristics comprises selecting candidateweight values corresponding to a group of ranking results with thehighest sum or average value of actual ranking scores as final weightvalues of the effective characteristics.

Embodiments of the present disclosure relate to an optimization methodof ranking search results. The embodiments may include acquiring rankingresults at a certain predetermined time point ranked based ontheoretical ranking scores of search results, wherein the theoreticalranking score is obtained based on a final weight of an effectivecharacteristic and an actual effective characteristic value of each ofthe search results, acquiring transaction data of a predetermined numberof search results ranked higher in the ranking results after thepredetermined time point and calculating actual ranking scores of thesearch results based on the transaction data, and comparing the actualranking score with the theoretical ranking score, and if a differencevalue thereof exceeds a threshold value, optimizing the final weight ofthe effective characteristic.

Further, the theoretical ranking score is a predicted deal conversionrate and the actual ranking score is an actual deal conversion rate, orthe theoretical ranking score is a predicted positive feedback rate andthe actual ranking score is an actual positive feedback rate.

Embodiments of the present disclosure further relate to a system ofranking search results. The embodiments may include a primitivecharacteristic set acquisition module for acquiring a set of primitivecharacteristics, wherein the primitive characteristics include presetcharacteristics that may have an influence on ranking of search results,an effective characteristic extraction module for extracting effectivecharacteristics from the set of primitive characteristics based onhistorical transaction data, wherein the effective characteristics referto characteristics determined based on the historical transaction datathat may have an influence on ranking of search results, an effectivecharacteristic weight determination module for determining an initialweight of each of the effective characteristics based on the historicaltransaction data, and training the initial weight using the historicaltransaction data and a predetermined training model to obtain a finalweight, and a ranking module for ranking the search results based on thefinal weight of the effective characteristic.

Further, the effective characteristic extraction module comprises a testproduct selection sub-module for selecting two groups of test productsbased on historical transaction data, one of which being products withdeal records and the other of which being products without deal records,a characteristic value calculation sub-module for respectivelyextracting associated data of the two groups of test products within acertain time period from the historical transaction data and calculatinga characteristic value of each primitive characteristic of the twogroups of test products using the associated data, and a comparisonsub-module for comparing characteristic values of the same primitivecharacteristic of the two groups of test products, and if a differencevalue thereof exceeds a threshold value, selecting the primitivecharacteristic as an effective characteristic.

Embodiments of the present disclosure relate to an optimization systemof ranking search results. The embodiments may include a candidateweight value acquisition module for respectively acquiring each group ofcandidate weight values of effective characteristics of search results,a theoretical ranking score calculation module for calculatingtheoretical ranking scores of search results at a certain predeterminedtime point respectively using each of the candidate weight values, andranking the search results based on the theoretical ranking scores toobtain each group of ranking results, a transaction data acquisitionmodule for respectively acquiring a predetermined number of searchresults ranked higher in each group of ranking results, and acquiringtransaction data of the search results after the predetermined timepoint, an actual ranking score calculation module for calculating actualranking scores of a predetermined number of search results ranked higherin each group of ranking results based on the transaction data, and afinal weight determination module for selecting candidate weight valuescorresponding to a group of ranking results with the highest actualranking score as final weight values of the effective characteristics.

Embodiments of the present disclosure relate to an optimization systemof ranking search results. Embodiments may include a theoretical rankingscore calculation module for acquiring ranking results at a certainpredetermined time point ranked based on theoretical ranking scores ofsearch results, wherein the theoretical ranking score is obtained basedon a final weight of an effective characteristic and an actual effectivecharacteristic value of each of the search results, an actual rankingscore calculation module for acquiring transaction data of apredetermined number of search results ranked higher in the rankingresults after the predetermined time point and calculating actualranking scores of the search results based on the transaction data, andan optimization module for comparing the actual ranking score with thetheoretical ranking score, and if a difference value thereof exceeds athreshold value, optimizing the final weight of the effectivecharacteristic.

Embodiments of the present disclosure include advantages over the priorart. Embodiments of the present disclosure select effectivecharacteristics that affect ranking results through historicaltransaction data, determine final weights of the effectivecharacteristics in combination with the historical transaction data, andfinally rank search results using these weights. In this process,besides that an initial weight of each of the effective characteristicswill be determined based on the historical transaction data, the initialweight also will be trained using the historical transaction data,thereby obtaining an optimized final weight to ensure the objectivityand accuracy of the final weight, thereby improving the objectivity andaccuracy of ranking results to prevent a user from continuouslyrequesting to acquire the remaining data or resending a new searchrequest to a server via a client side due to the fact that the usercannot obtain the expected search results caused by inaccurate ranking,thereby reducing the burden on the server and the occupation of networkresources as well as the transmission quantity of data.

In addition, when effective characteristics are selected, first, twogroups of test products with high and low deal rates as well as a highercontrast are selected as a test basis based on historical transactiondata. After characteristic values of the two groups of test products arerespectively calculated based on the historical transaction data, thetwo groups of products are compared for a difference betweencharacteristic values of the same primitive characteristic to determinethe influence of characteristics on the product deal rate, therebyaccurately selecting effective characteristics and improving theaccuracy of ranking.

In the optimization method and system of ranking search results of thepresent disclosure, the optimum weight value is determined or thedetermined weight value is optimized by use of transaction data at acertain time point and after the time point, i.e. a relatively optimizedmanner of ranking search results is determined or the existing manner ofranking search results is optimized by use of real historicaltransaction data, which allows ranking results to be more objective andaccurate, and also may prevent a user from continuously requesting toacquire the remaining data or resending a new search request to a servervia a client side due to the fact that the user cannot obtain theexpected search results caused by inaccurate ranking, thereby reducingthe burden on the server and the occupation of network resources as wellas the transmission quantity of data. Embodiments of the presentdisclosure may not be implemented to meet all the above advantages atthe same time.

Furthermore, embodiments of the present disclosure may pre-process theinformation of pages and the query phrase by deleting invalidcharacters, and/or word roots. Embodiments of the present disclosure mayspeed up searches, determine the sorting processes, and return accurateand relevant results.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is described with reference to the accompanyingfigures. The use of the same reference numbers in different figuresindicates similar or identical items.

FIG. 1 is a flow chart of an illustrative process for ranking searchresults.

FIG. 2 is a flow chart of an illustrative process for optimizing searchresults.

FIG. 3 is a schematic diagram of exemplary groups showing ranking andoptimizing search results.

FIG. 4 is a flow chart of an illustrative process for optimizing searchresults.

FIGS. 5-7 are schematic diagrams of illustrative computing architecturesthat enable ranking and optimizing search result.

DETAILED DESCRIPTION

Embodiments of the present disclosure are further described below indetail with reference to the drawings.

FIG. 1 is a flow chart of an illustrative process for ranking searchresults. At 102, a server may acquire a set of primitivecharacteristics, wherein the primitive characteristics include presetcharacteristics that may have an influence on ranking of search results.The set of primitive characteristics may be determined based onhistorical transaction data or empirically. In some embodiments, thecharacteristics may include a set of primitive characteristics tradingvolume, deal conversion rate, positive feedback rate, delivery speed,picture-word quality, transaction amount, etc.

In some embodiments, the set of primitive characteristics of searchresults may be preset, and may be directly acquired from a server orother databases when required, and historical transaction data also maybe acquired in real time from a server or database and extracted byreal-time analysis.

At 104, the server may extract effective characteristics from the set ofprimitive characteristics based on historical transaction data. In someembodiments, the effective characteristics may include characteristicsthat are determined based on the historical transaction data that mayhave an influence on ranking of search results.

In some embodiments, the historical transaction data may be directlyread from a server. In these instances, the extracting effectivecharacteristics from the set of primitive characteristics based onhistorical transaction data specifically may include selecting twogroups of test products based on historical transaction data, one ofwhich being products with deal records and the other of which beingproducts without deal records, respectively extracting associated dataof the two groups of test products within a certain time period from thehistorical transaction data and calculating a characteristic value ofeach primitive characteristic of the two groups of test products usingthe associated data, and comparing characteristic values of the sameprimitive characteristic of the two groups of test products, and if adifference value thereof exceeds a threshold value, selecting theprimitive characteristic as an effective characteristic.

In some embodiments, the associated data may be used to calculatespecific values of various characteristics in a set of primitivecharacteristics. Different characteristics need different associateddata, and specific associated data may be determined according tospecific needs. For example, for this characteristic-trading volume, itsrequired data is the number of deals within a predetermined time period,so its associated data is the number of deals. For another example, forpositive feedback rate, its required raw data includes the total numberof feedbacks and the number of positive feedbacks within a predeterminedtime period, so its associated data includes the total number offeedbacks and the number of positive feedbacks.

In some embodiments, the calculation formula of a characteristic valueof each primitive characteristic in a set of primitive characteristicsmay be determined according to the practical situation. In someinstances, how to effectively represent the characteristic may beconsidered. For example, for this characteristic-trading volume, if itscharacteristic value is directly represented by the value of tradingvolume, then its theoretical value may be from 0 to any natural number.In some instances, when a specific value is obtained, the problem oftencannot be explained by just comparing the difference among values.

For example, trading volumes are 0 and 1 respectively. In theseinstances, although the difference between characteristic values of bothtrading volume is 1, the difference indicates whether there is atransaction. For another example, trading volumes are 100 and 101respectively. Although the difference is still 1, the differenceindicates one transaction between the trading volumes. Accordingly, acalculation formula may be reset for the characteristic. For example, afinal characteristic value may be calculated by taking the tradingvolume as a parameter instead of a characteristic value.

For example, suppose that the trading volume is n, then equation1−1/(1+n) may be employed to calculate the characteristic value.Correspondingly, if the trading volumes are 0 and 1 respectively, thecharacteristic values are 0 and 0.5 respectively. While characteristicvalues of 100 transactions and 101 transactions are 0.9901 and 0.9902respectively. In this way, changes of trading volumes may be moreeffectively represented.

It may be understood that a criterion for selecting two groups of testproducts in the above-mentioned step is whether there are deal records.In some embodiments, in order to improve a contrast of two groups oftest products, increase a range of selected products and improveaccuracy of results, one group may be products with deal records higherthan a first threshold value, while the other group is products withoutdeal records or with deal records lower than a second threshold value.In these instances, the first threshold value and the second thresholdvalue may be set according to the practical situation. The firstthreshold value may be set as high as possible, while the secondthreshold value may be set as low as possible. This may ensure that twogroups of test products have larger differences, thus facilitatingsubsequent accurate extraction of effective characteristics.

It may be understood that test products may be selected based on somecharacteristics in addition to the historical transaction data (e.g.,deal records). According to some embodiments of the present disclosure,search results are ranked in an e-commerce website and search resultsthat are expected by a user are provided. This increases the probabilityof product purchase and prevents the user from repeatedly sending asearch query to a server. Embodiments of the present disclosure includea deal conversion rate of a product in addition to correlations. Thedeal conversion rate of a product may be a probability that a certainproduct is purchased after appearing in search results. In someembodiments, this characteristic (e.g., the deal conversion rate of aproduct) may have a greater weight on ranking results. In someembodiments, a user submits a query and receives search resultsincluding a product. The greater probability the user purchases theproduct, the greater probability other users who submit the same orsimilar query purchase the same product.

In some embodiments, test products may be selected based on dealconversion rates. The server may extract transaction data within apredetermined time period from historical transaction data and calculatea deal conversion rate of each product within the predetermined timeperiod. The server may then select two groups of products with adifference value of deal conversion rates greater than a threshold valueas test products. The server may extract transaction data of the twogroups of test products within a certain time period following thepredetermined time period from the historical transaction data andcalculate a characteristic value of each primitive characteristic in aset of primitive characteristics of the two groups of test products. Theserver may compare characteristic values of the same primitivecharacteristic of the two groups of test products. In response to adetermination that a difference value thereof exceeds a threshold value,the server may select the primitive characteristic as an effectivecharacteristic.

A predetermined time period may be set according to actual needs. Insome instances, to save the calculation time and reduce the calculationamount, a shorter period may be set. In other instances, consideringaccuracy and capacity of the server, a longer period may be set. Forexample, 1 day, 3 days, 10 days, 30 days or other periods may set. Acertain time period following the predetermined time period also may beset according to actual needs. In some instances, in order to ensure thematching of calculated results, the certain time period may be set basedon the predetermined time period. For example, the certain time periodmay be set as the same as the predetermined time period.

In some embodiments, the server may select two groups of products with agreater difference value of deal conversion rates. The two groups may beselected as test products based on deal conversion rates within apredetermined time period. In some instances, a difference value of afirst conversion value and a second conversion value may be set to athreshold value. If the deal conversion rate of a group of products ishigher than the first conversion value but lower than the secondconversion value, the two groups of products may be selected as testproducts. The server may then calculate a characteristic value of eachprimitive characteristic in a set of primitive characteristics of thetwo groups of test products. The characteristic value may be calculatedusing transaction data of the two groups of test products within acertain time period following the predetermined time period. Ifcharacteristic values of the same primitive characteristic of two groupsof test products have a greater difference value, the primitivecharacteristic may serve as an effective characteristic. For example,the difference value exceeds a set threshold value. Accordingly, twogroups of test products with a significant difference of deal conversionrates are selected. The greater the difference value of characteristicvalues of a certain primitive characteristic indicates that theinfluence of the primitive characteristic on whether deals are reachedon products is greater. Primitive characteristics may be screened inthis way to extract related effective characteristics, thus allowingranking results to be more accurate.

The selection of effective characteristics by the above-mentionedseveral methods may depend on two groups of test products with a higherdeal contrast. For example, one group is products with deal records,while the other group is products without deal records. For anotherexample, one group is products with a higher deal conversion rate, whilethe other group is products with a lower deal conversion rate. If acertain characteristic has a greater influence on deals of a product,the characteristic values calculated based on transaction data also willhave a larger difference. If a certain characteristic has little oralmost no influence on deals of a product, the characteristic values oftwo groups of products with a higher deal contrast also will have littleor almost no difference. Consequently, effective characteristics may bebetter screened by this method, thus improving the ranking accuracy ofsubsequent search results.

It may be understood that test products also may be selected based onother characteristics. For example, if ranking results depend more onpositive feedback degree, two groups of products with a largerdifference of positive feedback degree may be selected as test products.Then, characteristic values of primitive characteristics of two groupsof test products are calculated in a similar way as mentioned above toextract primitive characteristics with a larger difference ofcharacteristic values as effective characteristics. Similarly, ifranking results depend more on trading volumes, two groups of productswith a larger difference of trading volumes may be selected as testproducts. Certain selections may be performed in a similar process asdescribed above, which is not described in detail here.

At 106, the server may determine an initial weight of each of theeffective characteristics based on the historical transaction data, andtraining the initial weight using the historical transaction data and apredetermined training model to obtain a final weight. In someembodiments, both initial weights and final weights of various effectivecharacteristics may be determined by means of model training. It may beunderstood that initial weights also may be empirically set. Take amultidimensional linear model for example, initial weights of variouseffective characteristics first may be determined by means ofmultidimensional linear fitting. These initial weights may then besubstituted into a calculation formula and combined with historicaltransaction data to calculate theoretical data. The theoretical data maybe compared with actual data. The smaller the difference, the moreaccurately the initial weights are determined. If the difference iswithin a predetermined range, the initial weights may be selected asfinal weights of effective characteristics. If the difference is notwithin a predetermined range, initial weights may be re-determined andcalculated by the foregoing method until the difference is reduced to bewithin a predetermined range.

Take deals of a product for example, first, theoretical deals may becalculated based on initial weights and historical transaction data.Then, the calculated theoretical deals may be compared with actualdeals. The smaller the difference, the more accurately the initialweights are determined. The initial weights may serve as final weightsof effective characteristics. In some embodiments, weights may bere-determined until the determined weight values allow a differencebetween the theoretical deals and the actual deals to be minimized orwithin a predetermined range. In a particular training, deals may berepresented by deal conversion rates or indicators whether the dealsoccur. It may be understood that model trainings may be performed bymultiple machine learning methods, which will not be expounded in thepresent disclosure.

At 108, the server may rank the search results based on the final weightof the effective characteristic. In some embodiments, the ranking thesearch results based on the final weight of the effective characteristiccomprises determining actual effective characteristic values of thesearch results, calculating predicted deal conversion rates of thesearch results based on the final weight of the effective characteristicand the actual effective characteristic values, and ranking the searchresults based on the predicted deal conversion rates.

In some embodiments, ranking may be performed based on other factors.These factors may include positive feedback rate of search results, andother factors that may be determined according to different rankingpurposes. When ranking purposes are different, the factors for rankingmay be different. Therefore, ranking results may change correspondingly.Then, ranking scores of various search results may be calculated and thesearch results may be ranked based on the foregoing method.

The foregoing method will be described below in detail with reference toparticular examples. Suppose that the five characteristics included in aset of extracted primitive characteristics are a trading volume, dealconversion rate, positive feedback rate, delivery speed and picture-wordquality.

Suppose that a predetermined time period is 30 days. As illustrated inTable 1, it may be determined that the historical transaction data to beacquired comprises the number of deals, number of exposures, number ofpositive feedbacks, total number of feedbacks, days of delivery, thenumber of pictures and number of words. After these historicaltransaction data are acquired, calculation may be performed according toa calculation method to determine a characteristic value of eachprimitive characteristic.

TABLE 1 Calculation method of characteristic values and raw data Name ofcharac- Calculation SN Raw data teristics method 1 Number of deals (n)within 30 Trading 1 − 1/(1 + n) days volume 2 Number of deals (n) within30 Deal (n + 0.2)/(d + 10) days and number of exposures conversion (d)within 30 days rate 3 Number of positive feedbacks (g) Positive (g +8.5)/(f + 10) within 30 days and total number feedback of feedbacks (f)within 30 days rate 4 Days of delivery (t) Delivery if(t > 3) 3/t; else1; speed 5 Number of pictures (i) and Picture- (1 − 1/(1 + number ofwords (w) word i))*(1 − 1/(1 + w)) quality

Suppose that characteristic values of the five characteristics of thetwo groups of test products calculated based on the above historicaltransaction data are initial characteristic values. Two groups of testproducts with a higher contrast may be selected based on the calculatedinitial characteristic values. Suppose further that one group isproducts with a deal conversion rate of more than 70% while the othergroup is products with a deal conversion rate of lower than 1%. It maybe understood that, if test products are selected herein dependent upondeal conversion rate, only the deal conversion rate may be calculatedand characteristic values of other characteristics may not becalculated.

The historical transaction data of the two groups of test productswithin several time periods following the above 30 days may be acquired.For example, it may be historical transaction data within a week andalso may be historical transaction data still within 30 days, andcharacteristic values of the five characteristics of the two groups oftest products may be calculated based on these historical transactiondata and assumed to be validated characteristic values.

Then, the validated characteristic values of the same characteristic ofthe two groups of test products may be respectively compared. If adifference value thereof exceeds a threshold value, the characteristicmay be determined as an effective characteristic. Suppose further thatthe threshold value is 0.3, the above comparison indicates that the fivecharacteristics of the two groups of test products. The trading volume,deal conversion rate, positive feedback rate, delivery speed andpicture-word quality have a difference value of 0.6, 0.9, 0.8, 0.5 and0.02 respectively. Accordingly, it follows that the finally selectedeffective characteristics are trading volume, deal conversion rate,positive feedback rate and delivery speed.

The final weights of the four effective characteristics may bedetermined by means of model training based on historical transactiondata, and actual values of the four effective characteristics in searchresults may be acquired. Then, ranking scores of various search resultsmay be calculated based on the determined final weights and actualvalues of the effective characteristics. Accordingly, the search resultsare ranked based on the ranking scores.

Embodiments of the present disclosure select effective characteristicsthat affect ranking results through historical transaction data,determine final weights of the effective characteristics in combinationwith the historical transaction data, and finally rank search resultsusing these weights. In this process, an initial weight of each of theeffective characteristics may be determined based on the historicaltransaction data. Then, the initial weight may be trained using thehistorical transaction data. This obtains an optimized final weight toensure the objectivity and accuracy of the final weight, and improvesthe objectivity and accuracy of ranking results.

In addition, when effective characteristics are selected, first, twogroups of test products with high and low deal rates may be selected,and a higher contrast may be also selected as a test basis based onhistorical transaction data. After characteristic values of the twogroups of test products are respectively calculated based on thehistorical transaction data, the two groups of products may be comparedfor a difference between characteristic values of the same primitivecharacteristic to determine the influence of characteristics on theproduct deal rate, thereby accurately selecting effectivecharacteristics and improving the ranking accuracy.

FIG. 2 is a flow chart of an illustrative process for optimizing searchresults. At 202, the server may respectively acquire each group ofcandidate weight values of effective characteristics of search results.There are at least two groups of candidate weight values of effectivecharacteristics and there also may be three or four groups thereof.

At 204, the server may calculate theoretical ranking scores of searchresults at a certain predetermined time point respectively using each ofthe candidate weight values, and ranking the search results based on thetheoretical ranking scores to obtain each group of ranking results.Theoretical ranking scores may be specific scores of deal conversionrates, predicted positive feedback rates or other characteristics or acombination of characteristics of search results, which are determinedbased on actual ranking purposes and not limited in the presentdisclosure.

In some embodiments, the theoretical ranking scores are illustrated bytaking predicted deal conversion rates as an example in an embodiment ofthe present disclosure. The server may calculate predicted dealconversion rates of search results at a certain predetermined time pointrespectively using each of the candidate weight values, and ranking thesearch results based on the predicted deal conversion rates to obtaineach group of ranking results.

When search results at a certain predetermined time point aredetermined, effective characteristics of the search results may beacquired first, and effective characteristic values of these searchresults may be calculated based on actual data. Different predicted dealconversion rates of the search results may be calculated based oneffective characteristic values respectively in combination with eachgroup of candidate weight values, and different ranking results may beobtained based on the different predicted deal conversion rates.

For example, suppose that there are in total four search results at acertain predetermined time point including: a, b, c and d. Supposefurther that there are two groups of candidate weight values. There is apossibility that the ranking results calculated based on one group ofweight values are: a, b, c and d, while the ranking results calculatedbased on the other group of weight values are: d, c, a and b.

At 206, the server may respectively acquire a predetermined number ofsearch results ranked higher in each group of ranking results, andacquire transaction data of the search results after the predeterminedtime point. A predetermined number of specific values ranked higher maybe determined based on the number of actual search results and thecalculation capability of a system. For example, if the number of actualsearch results is bigger than a predetermined value while thecalculation capability of a system is less than one predetermined value.The predetermined number may be set to a smaller value, e.g. 2%, 4%,etc. If allowed by the calculation capability of a system, apredetermined number may be set to a larger value, e.g. 10%, etc. Ofcourse, the more the data is, the more objective and accurate resultsmay be provided. Thus, multiple predetermined numbers also may be set,e.g. 2%, 4%, 6%, 8%, 10%, etc.

The specific range of transaction data after a predetermined time pointmay be set according to a particular condition. For example, the rangemay cover transaction data within one week after a predetermined timepoint and also may cover transaction data within ten days, twenty daysor other time periods, provided that the transaction data is thatcapable of being acquired after a predetermined time point.

At 208, the server may calculate actual ranking scores of apredetermined number of search results ranked higher in each group ofranking results based on the transaction data. In some embodiments, theactual ranking scores may be those of search results calculated based onactual data using the same method as that for calculating theoreticalranking scores. For example, where theoretical ranking scores arepredicted deal conversion rates, actual ranking scores herein mayinclude actual deal conversion rates.

At 210, the sever may select candidate weight values corresponding to agroup of ranking results with the highest actual ranking score as finalweight values of the effective characteristics.

In some embodiments, theoretical ranking scores of various searchresults may be calculated during ranking. Accordingly, the higher thetheoretical ranking scores, the more frontward the ranking order. Thehigher the actual ranking scores is, the more conformable the rankingresults to the practical situation is indicated to the user and rankingherein is more accurate. It may be understood that the highest actualranking scores may mean that actual ranking scores of search resultsselected from a certain ranking result are higher than those of searchresults at the same position in other ranking results. However, this isa relatively ideal ranking result and such an optimized ranking resultmay not be obtained in some embodiments, thus the highest actual rankingscores may refer to the highest sum or the average value of actualranking scores so as to simplify the calculation flow.

Take the foregoing two ranking results a, b, c and d, as well as d, c, aand b for example. Suppose that the basis of ranking is deal conversionrates. As results, search results may be ranked based on values ofpredicted deal conversion rates, and then the search results ranked atthe first two positions may be selected from each group of rankingresults, which are respectively a and b, and d and c. Actual dealconversion rates of the four search results (a, b, c and d) may becalculated based on transaction data, which are respectively 5%, 4%, 3%and 2%. Then it follows that the average value of actual deal conversionrates of a and b is 4.5%, which is higher than the average value 2.5% ofactual deal conversion rates of d and c. Accordingly, candidate weightvalues corresponding to the group of ranking results a, b, c and d maybe taken as final weight values of effective characteristics.

The foregoing embodiments related to optimizing search results aredescribed below in detail by taking deal conversion rates as an examplein combination with particular examples.

Suppose that a group of search results may be obtained by searching akeyword at a time point T. According to the foregoing embodiments,effective characteristics of the group of search results may be constantand effective characteristic values thereof may also be constant.Suppose that effective characteristics have two groups of final weights.As a result, predicted deal conversion rates of search results may becalculated based on the two groups of final weight values, and thensearch results may be ranked based on the values of predicted dealconversion rates. Suppose that there are 50 search results, and twogroups of ranking results may be obtained as a result of differenceamong weight values. The two groups of ranking results are supposed tobe N and O (as shown in FIG. 3), for which, the average value of actualdeal conversion rates of the first x % of search results may becalculated within a time period after T (e.g. within one week). The factthat the average value of actual deal conversion rates of the first x %of ranking result N is higher than that of actual deal conversion ratesof the first x % of ranking result O indicates that deal conversionrates of ranking results predicted via a ranking result N at the timepoint T are closer to actual results. That is, if returned to the timepoint T, weight values employed by the ranking result N may be appliedto rank search results, and thus may rank higher the search results withhigher deal conversion rates after the time point T, thereby increasingthe presentation opportunity of these search results and promoting moretransactions.

In some embodiments, the difference between two groups of rankingresults may be calculated by selecting different x values to obtain morecomprehensive and objective comparison. For example, the average valueof actual deal conversion rates of the first 2% of a commodity iscalculated; then that of the first 4%, 6%, 8% . . . is calculated (asshown in Table 2). The two ranking results then may be compared atdifferent points. It follows that the predictive effect of the rankingresult N is significantly better than that of the ranking result O. Itmay be understood that the data may further be plotted as a curve of theaverage value of actual deal conversion rates to more visually observethe effect difference there between.

TABLE 2 The average value of actual deal conversion rates of the first x% of commodity for two ranking results (N and O) x % 2% 4% 6% 8% 10% . .. N 0.038671 0.037019 0.036061 0.035228 0.034294 . . . O 0.0311060.030587 0.029903 0.029179 0.028548 . . .

In some embodiments, significance validation may also be furtherperformed to ensure that the final weights of effective characteristicsemployed by the ranking result N have statistical other than occasionalsignificance over those employed by the ranking result O in terms ofeffect improvement. Significance validation may be achieved through manyprior methods, e.g. take T-test as an example. T-test may be performedto compare the average value of two groups of samples. A P value inT-test represents a probability that that there is a difference betweenthe average values of two samples is false. It is generally believedthat the difference between two samples is very significant whenP<=0.01. Suppose that there are 50 average values of actual dealconversion rates. As results, T-test may be applied to the 50 averagevalues of actual deal conversion rates of two ranking results in Table1, and the P value obtained is about 8.7E-07, which is far smaller than0.01. Accordingly, in the case of statistical significance, the finalweights of effective characteristics employed by the ranking result Nmay be optimized more significantly than those employed by the rankingresult O.

It may be understood that deal conversion rates are used as an examplefor illustration in the foregoing method, although ranking andoptimization may be performed based on other characteristics (e.g.,positive feedback rate, delivery speeds, and etc.). In some embodiments,ranking and optimization may also be performed based on comprehensivecharacteristics.

In some embodiments, final weights of the effective characteristics havenot yet been determined, and final weights of a group of optimumeffective characteristics need to be selected from multiple groups ofpossible results. It may be understood that some embodiments may beimplemented when the optimization may be performed based on the factthat final weights of the effective characteristics have beendetermined.

FIG. 4 is a flow chart of an illustrative process for optimizing searchresults. At 402, the server may acquire ranking results at a certainpredetermined time point ranked based on theoretical ranking scores ofsearch results, wherein the theoretical ranking score is obtained basedon a final weight of an effective characteristic and an actual effectivecharacteristic value of each of the search results.

At 404, the server may acquire transaction data of a predeterminednumber of search results ranked higher in the ranking results after thepredetermined time point and calculating actual ranking scores of thesearch results based on the transaction data.

At 406, the server may compare the actual ranking score with thetheoretical ranking score, and if a difference value thereof exceeds athreshold value, optimizing the final weight of the effectivecharacteristic.

A final weight of an effective characteristic may be optimized by meansof model training mentioned in the above ranking method. In someinstances, a final weight of each effective characteristic may bedetermined and optimized by acquiring historical transaction data incombination with a training model, which may not be described in detailherein. A threshold value also may be set based on actualcharacteristics corresponding to an actual ranking score and atheoretical ranking score. For example, if the actual ranking score andthe theoretical ranking score are respectively an actual deal conversionrate and a predicted deal conversion rate, their threshold values may bedetermined by the allowable difference value range of the dealconversion rates in general case (e.g., 0.2 or other values).

In some embodiments, the optimum weight value may be determined by useof transaction data at a certain time point and after the time point. Insome embodiments, the determined weight value may be optimized by use oftransaction data at a certain time point and after the time point. Insome instances, a relatively optimized manner of ranking search resultsmay be determined by use of real historical transaction data, whichallows ranking results to be more objective and accurate. In someinstances, the existing manner of ranking search results may beoptimized by use of real historical transaction data.

FIGS. 5-7 are schematic diagrams of illustrative computing architecturesthat enable ranking and optimizing search result. FIG. 5 is a diagram ofa computing device 500. The computing device 500 may be a user device ora server for a multiple location login control. In one exemplaryconfiguration, the computing device 500 includes one or more processors502, input/output interfaces 504, network interface 506, and memory 508.

The memory 508 may include computer-readable media in the form ofvolatile memory, such as random-access memory (RAM) and/or non-volatilememory, such as read only memory (ROM) or flash RAM. The memory 408 isan example of computer-readable media.

Computer-readable media includes volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules, or other data. Examples of computer storagemedia include, but are not limited to, phase change memory (PRAM),static random-access memory (SRAM), dynamic random-access memory (DRAM),other types of random-access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disk read-only memory(CD-ROM), digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other non-transmission medium that maybe used to store information for access by a computing device. Asdefined herein, computer-readable media does not include transitorymedia such as modulated data signals and carrier waves.

Turning to the memory 508 in more detail, the memory 508 may include aprimitive characteristic set acquisition module 510, an effectivecharacteristic extraction module 512, an effective characteristic weightdetermination module 514 and a ranking module 516.

The primitive characteristic set acquisition module 510 is configured toacquire a set of primitive characteristics, wherein the primitivecharacteristics include preset characteristics that may have aninfluence on ranking of search results. The effective characteristicextraction module 512 is configured to extract effective characteristicsfrom the set of primitive characteristics based on historicaltransaction data, wherein the effective characteristics refer tocharacteristics determined based on the historical transaction data thatmay have an influence on ranking of search results. In some embodiments,the effective characteristic extraction module comprises a test productselection sub-module, a characteristic value calculation sub-module anda comparison sub-module. The test product selection sub-module isconfigured to select two groups of test products based on historicaltransaction data, one of which is products with deal records and theother of which is products without deal records. The characteristicvalue calculation sub-module is configured to respectively extractassociated data of the two groups of test products within a certain timeperiod from the historical transaction data and calculate acharacteristic value of each primitive characteristic of the two groupsof test products using the associated data. The comparison sub-module isconfigured to compare characteristic values of the same primitivecharacteristic of the two groups of test products. If a difference valuethereof exceeds a threshold value, the server may select the primitivecharacteristic as an effective characteristic.

The effective characteristic weight determination module 512 isconfigured to determine an initial weight of each of the effectivecharacteristics based on the historical transaction data, and train theinitial weight using the historical transaction data and a predeterminedtraining model to obtain a final weight.

The ranking module 514 is configured to rank the search results based onthe final weight of the effective characteristic.

In some embodiments, as shown in FIG. 6, the memory 508 may include acandidate weight value acquisition module 602, a theoretical rankingscore calculation module 604, a transaction data acquisition module 606,an actual ranking score calculation module 608 and a final weightdetermination module 610.

The candidate weight value acquisition module 602 is configured torespectively acquire each group of candidate weight values of effectivecharacteristics of search results.

The theoretical ranking score calculation module 604 is configured tocalculate theoretical ranking scores of search results at a certainpredetermined time point respectively using each of the candidate weightvalues, and rank the search results based on the theoretical rankingscores to obtain each group of ranking results.

The transaction data acquisition module 606 is configured torespectively acquire a predetermined number of search results rankedhigher in each group of ranking results, and acquire transaction data ofthe search results after the predetermined time point.

The actual ranking score calculation module 608 is configured tocalculate actual ranking scores of a predetermined number of searchresults ranked higher in each group of ranking results based on thetransaction data.

The final weight determination module 610 is configured to selectcandidate weight values corresponding to a group of ranking results withthe highest actual ranking score as final weight values of the effectivecharacteristics.

In some embodiments, as shown in FIG. 7, the memory 508 may include atheoretical ranking score calculation module 702, an actual rankingscore calculation module 704 and an optimization module 706.

The theoretical ranking score calculation module 702 is configured toacquire ranking results at a certain predetermined time point rankedbased on theoretical ranking scores of search results, wherein thetheoretical ranking score may be obtained based on a final weight of aneffective characteristic and an actual effective characteristic value ofeach of the search results.

The actual ranking score calculation module 704 is configured to acquiretransaction data of a predetermined number of search results rankedhigher in the ranking results after the predetermined time point and tocalculate actual ranking scores of the search results based on thetransaction data.

The optimization module 706 is configured to compare the actual rankingscore with the theoretical ranking score. If a difference value thereofexceeds a threshold value, the optimization module 706 may optimize thefinal weight of the effective characteristic.

Various embodiments of the specification are described in a progressiveway, and each of the embodiments focuses on the differences from otherembodiments. Thus, the same and similar parts among various embodimentsmay be referred to each other.

Various embodiments are described herein to explain the presentdisclosure, and the description of the above embodiments is only usedfor the purpose of assisting in understanding the present disclosure.Meanwhile, those of ordinary skill in the art may make changes in termsof particular embodiments and application scopes based on the ideas ofthe present disclosure. In summary, the contents of the specificationmay not be interpreted as limiting the present disclosure.

The embodiments are merely for illustrating the present disclosure andare not intended to limit the scope of the present disclosure. It shouldbe understood for persons in the technical field that certainmodifications and improvements may be made and should be consideredunder the protection of the present disclosure without departing fromthe principles of the present disclosure.

What is claimed is:
 1. A method of ranking search results, the methodcomprising: acquiring, by a server, multiple primitive characteristicsthat include multiple preset characteristics having influence on searchresult ranking, the primitive characteristics being based on historicaltransaction data; extracting multiple effective characteristics from themultiple primitive characteristics based on historical transaction data,the extracting multiple effective characteristics comprising: extractingtransaction data within a predetermined time period from the historicaltransaction data, calculating a deal conversion rate of individualproducts within the predetermined time period; and selecting aneffective characteristic based on the deal conversion rate; determiningan initial weight of an individual characteristic of the multipleeffective characteristics based on the historical transaction data;training the initial weight using the historical transaction data and apredetermined training model to obtain a final weight; and rankingsearch results based on the final weight.
 2. The method of claim 1,wherein the extracting the multiple effective characteristics from themultiple primitive characteristics based on the historical transactiondata comprises: selecting a first group of test products based onhistorical transaction data, the first group of test products havingdeal records; selecting a second group of test products based on thehistorical transaction data, the second group of test products nothaving deal records; extracting associated transaction data of the firstgroup of test products and the second group of test productsrespectively within a predetermined time period from the historicaltransaction data; selecting an effective characteristic based on acomparison between the associated transaction data of the first group oftest products and the associated transaction data of the second group oftest products.
 3. The method of claim 2, wherein the selecting theeffective characteristic based on the comparison between the associatedtransaction data of the first group of test products and the associatedtransaction data of the second group of test products comprises:calculating, using the associated data, a first characteristic value ofan individual characteristic of multiple primitive characteristics ofthe first group of test products; calculating, using the associateddata, a second characteristic value of an individual characteristic ofmultiple primitive characteristics of the second group of test products;determining a difference between the first characteristic value andsecond characteristic value; and selecting, in response to adetermination that the difference is greater than a predetermined value,the individual characteristic as an effective characteristic.
 4. Themethod of claim 1, wherein the selecting the effective characteristicbased on the deal conversion rate comprises: selecting a first group oftest products and a second test group of products, a difference of dealconversion rates of the first group of test products and the secondgroup of test products being greater than a predetermined value;extracting, from the historical transaction data, transaction data ofthe first groups of test products and the second groups of test productswithin a certain time period after the predetermined time period;calculating, using the transaction data, a first characteristic value ofan individual characteristic of multiple primitive characteristics ofthe first group of test products; calculating, using the transactiondata, a second characteristic value of an individual characteristic ofmultiple primitive characteristics of the second group of test products;determining a difference between the first characteristic value andsecond characteristic value; and selecting, in response to adetermination that the difference is greater than a predetermined value,the individual characteristic as an effective characteristic.
 5. Themethod of claim 1, wherein the determining the initial weight of theindividual characteristic of the multiple effective characteristicsbased on the historical transaction data and the training the initialweight using the historical transaction data and the predeterminedtraining model to obtain the final weight comprise: determining aninitial weight of an effective characteristic; substituting thehistorical transaction data and the initial weight into thepredetermined training model to calculate theoretical data; determininga difference between the theoretical data with actual data; anddetermining, in response to a determination that the difference is lessthan a predetermined value, that the initial weight is a final weight ofthe effective characteristic.
 6. The method of claim 1, wherein theranking the search results based on the final weight comprises:determining actual effective characteristic values of the searchresults; calculating predicted deal conversion rates of the searchresults based on the final weight of the effective characteristic andthe actual effective characteristic values; and ranking the searchresults based on the predicted deal conversion rates.
 7. One or morecomputer-readable media storing computer-executable instructions that,when executed by one or more processors, instruct the one or moreprocessors to perform acts comprising: acquiring candidate weight valuesof effective characteristics of multiple search results, the candidateweight values being divided into multiple groups; calculatingtheoretical ranking scores of search results at a predetermined timepoint using the candidate weight values; ranking the search resultsbased on the theoretical ranking scores to obtain multiple groups ofranking results; acquiring a predetermined number of search results inan individual group of the multiple groups of ranking results; acquiringtransaction data of the search results after the predetermined timepoint; calculating actual ranking scores of the predetermined number ofsearch results in the individual group of the multiple groups of rankingresults based on the transaction data; and selecting candidate weightvalues corresponding to a group of ranking results that have an actualranking score greater than a predetermined value as final weight valuesof the effective characteristics.
 8. The one or more computer-readablemedia of claim 7, wherein the theoretical ranking score is a predictedvalue of a single characteristic or a predicted value of a combinationof characteristics, and the actual ranking score is an actual value of asingle characteristic or an actual value of a combination ofcharacteristics that is corresponding to the theoretical ranking score.9. The one or more computer-readable media of claim 8, wherein thetheoretical ranking score is a predicted deal conversion rate, and theactual ranking score is an actual deal conversion rate.
 10. The one ormore computer-readable media of claim 8, wherein the theoretical rankingscore is a predicted positive feedback rate, and the actual rankingscore is an actual positive feedback rate.
 11. The one or morecomputer-readable media of claim 7, wherein the actual ranking score isa highest sum or average value of actual ranking scores.
 12. A method ofranking search results, the method comprising: acquiring, by a server ata predetermined time point, ranking results ranked based on theoreticalranking scores of multiple search results, the theoretical ranking scorebeing obtained based on a final weight of an effective characteristicand an actual effective characteristic value of each of the searchresults; acquiring transaction data of a predetermined number of searchresults of the multiple search results after the predetermined timepoint, the predetermined number of search results being ranked higherthan other search results of the multiple search results; calculatingactual ranking scores of the search results based on the transactiondata; determining a difference between the actual ranking score and thetheoretical ranking score; and optimizing, in response to adetermination that the difference exceeds a threshold value, the finalweight of the effective characteristic.
 13. The method of claim 12,wherein the theoretical ranking score is a predicted deal conversionrate, and the actual ranking score is an actual deal conversion rate.14. The method of claim 12, wherein the theoretical ranking score is apredicted positive feedback rate, and the actual ranking score is anactual positive feedback rate.
 15. A system of ranking search results,the system comprising: one or more processors; and a memory device tomaintain a plurality of components executable by the one or moreprocessors, the plurality of components comprising: a primitivecharacteristic set acquisition module configured to acquire multipleprimitive characteristics that include multiple preset characteristicshaving influence on search result ranking, the primitive characteristicsbeing based on historical transaction data, an effective characteristicextraction module configured to extract multiple effectivecharacteristics from the multiple primitive characteristics based onhistorical transaction data, the extracting multiple effectivecharacteristics comprising: extracting transaction data within apredetermined time period from the historical transaction data,calculating a deal conversion rate of individual products within thepredetermined time period; and selecting an effective characteristicbased on the deal conversion rate, an effective characteristic weightdetermination module configured to: determine an initial weight of anindividual characteristic of the multiple effective characteristicsbased on the historical transaction data, and train the initial weightusing the historical transaction data and a predetermined training modelto obtain a final weight, and a ranking module configured to rank searchresults based on the final weight.
 16. The system of claim 15, whereinthe effective characteristic extraction module comprises: a test productselection sub-module configured to: select a first group of testproducts based on historical transaction data, the first group of testproducts having deal records, and select a second group of test productsbased on the historical transaction data, the second group of testproducts not having deal records; a characteristic value calculationsub-module configured to extract associated transaction data of thefirst group of test products and the second group of test productswithin a predetermined time period from the historical transaction data;and a comparison sub-module configured to select an effectivecharacteristic based on a comparison between the associated transactiondata of the first group of test products and the associated transactiondata of the second group of test products.
 17. The system of claim 15,wherein the determining the initial weight of the individualcharacteristic of the multiple effective characteristics based on thehistorical transaction data and the training the initial weight usingthe historical transaction data and the predetermined training model toobtain the final weight comprise: determining an initial weight of aneffective characteristic; substituting the historical transaction dataand the initial weight into the predetermined training model tocalculate theoretical data; determining a difference between thetheoretical data with actual data; and determining, in response to adetermination that the difference is less than a predetermined value,that the initial weight is a final weight of the effectivecharacteristic.
 18. The system of claim 15, wherein the ranking thesearch results based on the final weight comprises: determining actualeffective characteristic values of the search results; calculatingpredicted deal conversion rates of the search results based on the finalweight of the effective characteristic and the actual effectivecharacteristic values; and ranking the search results based on thepredicted deal conversion rates.